Network Performance Monitoring with Flexible Models of Multi-Point Passive Measurements

Many management actions for networking infrastructures require to simultaneously consider the state of several network elements. This is particularly critical in the case of reconfigurable deployments, such as Virtual or Software-Defined Networks, to scale the affected equipment up and prevent performance bottlenecks. In this light, we present dPRISMA (distributed Passive Retrieval of Information, and Statistical Multipoint Analysis), a passive monitoring system intended to fit statistical models for network measurements and raise alarms in the case of extreme behaviors. As distinguishing features, dPRISMA relies on cost-effective multi-point network measurements, and is able to select a suitable parametric model optimizing the trade-off between fitting and complexity. Therefore, it can (i) correlate records collected from several vantage points and detect where performance issues are most likely to appear; (ii) adjust alarms in terms of the probability of events; and (iii) adapt its behavior to dynamic network conditions while presenting a fair identification of anomalous situations. We evaluate dPRISMA with experiments both in virtual environments and with real-world data to provide evidences of its applicability.